CN107688959B - Breakpoint list processing method, storage medium and server - Google Patents
Breakpoint list processing method, storage medium and server Download PDFInfo
- Publication number
- CN107688959B CN107688959B CN201710605626.9A CN201710605626A CN107688959B CN 107688959 B CN107688959 B CN 107688959B CN 201710605626 A CN201710605626 A CN 201710605626A CN 107688959 B CN107688959 B CN 107688959B
- Authority
- CN
- China
- Prior art keywords
- list
- target
- attribute
- breakpoint
- score
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0201—Market modelling; Market analysis; Collecting market data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q30/00—Commerce
- G06Q30/02—Marketing; Price estimation or determination; Fundraising
- G06Q30/0241—Advertisements
- G06Q30/0251—Targeted advertisements
- G06Q30/0255—Targeted advertisements based on user history
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q40/00—Finance; Insurance; Tax strategies; Processing of corporate or income taxes
- G06Q40/08—Insurance
Abstract
The invention discloses a breakpoint list processing method, which is used for solving the problem that the business conversion rate of a sales agent is low due to the fact that the sales agent is difficult to distinguish the advantages and disadvantages of a breakpoint list. The method provided by the invention comprises the following steps: acquiring target breakpoint lists to be distributed to sales agents, wherein each target breakpoint list comprises more than one list attribute; calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list; and respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list; and sequentially distributing each target breakpoint list to the sales agents from high to low according to the list score corresponding to each target breakpoint list. The invention also provides a storage medium and a server.
Description
Technical Field
The invention relates to the technical field of vehicle insurance sales, in particular to a breakpoint list processing method, a storage medium and a server.
Background
Currently, a breakpoint list of users is generally collected on a car insurance sales website in order to dig out potential sales customers. The breakpoint list refers to data that a user browses and other related operations on a car insurance sales website and finally does not complete a transaction. For example, if the car owner wants to know the car insurance quotation, the car insurance quotation is made by inputting information such as names, license plates, mobile phones and the like into the car insurance sale website, but subsequent insurance application operation is not performed. The background system collects similar data called breakpoint lists, and then provides the breakpoint lists for the sales seat to follow up so as to promote the users in the breakpoint lists to carry out insurance application.
However, the collected breakpoint lists have large data volume, various attributes and different quality, and it is difficult to distinguish the merits of the breakpoint lists in the follow-up process of the sales agent, which results in a low service conversion rate of the sales agent.
Disclosure of Invention
The embodiment of the invention provides a breakpoint list processing method, a storage medium and a server, which can improve the service conversion rate of a sales agent.
In a first aspect, a method for processing a breakpoint list is provided, including:
acquiring target breakpoint lists to be distributed to sales agents, wherein each target breakpoint list comprises more than one list attribute;
calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list;
and respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
and sequentially distributing each target breakpoint list to the sales agents from high to low according to the list score corresponding to each target breakpoint list.
In a second aspect, a computer-readable storage medium is provided, which stores a computer program, and the computer program, when executed by a processor, implements the steps of the above-mentioned breakpoint list processing method.
In a third aspect, a server is provided, which includes a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor implements the following steps when executing the computer program:
acquiring target breakpoint lists to be distributed to sales agents, wherein each target breakpoint list comprises more than one list attribute;
calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list;
and respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
and sequentially distributing each target breakpoint list to the sales agents from high to low according to the list score corresponding to each target breakpoint list.
According to the technical scheme, the embodiment of the invention has the following advantages:
in the embodiment of the invention, firstly, each target breakpoint list to be distributed to the sales seat is obtained, and each target breakpoint list comprises more than one list attribute; then, calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list; then, executing the following steps for each target breakpoint list respectively: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list; and finally, sequentially distributing the target breakpoint lists to the sales agents according to the respective corresponding list scores of the target breakpoint lists from high to low. Therefore, the list attributes of the breakpoint lists can be respectively scored, and the list score of each breakpoint list is determined according to the score of each scored list attribute, so that the quality of the breakpoint lists is distinguished according to the list scores, the higher the list score is, the higher the quality of the breakpoint list is, when the breakpoint list is distributed to a sales seat, the breakpoint list is distributed from high to low according to the list score, namely, the breakpoint list with high quality is preferentially distributed. Therefore, the quality of the breakpoint list can be distinguished without manually distinguishing by the sales agent, so that the breakpoint list currently received by the sales agent is the breakpoint list with the currently best quality in the same batch, and the service conversion rate of the sales agent can be further improved.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings needed to be used in the embodiments or the prior art descriptions will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without inventive exercise.
Fig. 1 is a flowchart of an embodiment of a method for processing a breakpoint list according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a step 102 of a breakpoint list processing method in an application scenario according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of step 102 of a method for processing a breakpoint list in another application scenario according to an embodiment of the present invention;
fig. 4 is a schematic flow chart of presetting a preset attribute score in another application scenario in step 102 of the breakpoint list processing method according to the embodiment of the present invention;
FIG. 5 is a block diagram of an embodiment of a breakpoint list processing apparatus according to an embodiment of the present invention;
fig. 6 is a schematic diagram of a server according to an embodiment of the present invention.
Detailed Description
The embodiment of the invention provides a processing method of a breakpoint list, a storage medium and a server, which are used for solving the problem that a sales agent is difficult to distinguish the advantages and disadvantages of the breakpoint list, so that the service conversion rate of the sales agent is low.
In order to make the objects, features and advantages of the present invention more obvious and understandable, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is obvious that the embodiments described below are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Referring to fig. 1, an embodiment of a method for processing a breakpoint list according to an embodiment of the present invention includes:
101. acquiring target breakpoint lists to be distributed to sales agents, wherein each target breakpoint list comprises more than one list attribute;
in this embodiment, after the user performs the relevant operations on the car insurance sales website, the system collects the operation information of the user and generates each breakpoint list without completing the insurance application. Before distributing the breakpoint lists to the sales agents, the quality of the breakpoint lists needs to be evaluated, and the higher quality breakpoint lists are distributed to the sales agents as earlier as possible. This is because, when the number of visitors to the car insurance sales website is large, the data of the breakpoint list collected by the system in real time is very large, and the processing capacity or task capacity of the sales seat is limited. Therefore, the high-quality breakpoint list is provided for the sales agent to be processed, the service conversion rate of the sales agent to the breakpoint list can be effectively improved, and the performance of car insurance sales is improved.
It is understood that, in general, a high-quality breakpoint list usually has complete attributes, such as a first breakpoint list recording user names of users, and a second breakpoint list recording user names and phone numbers of users. Obviously, the list attribute of the second breakpoint list can help the sales agent to more smoothly perform sales and complete service conversion, so that the second breakpoint list has a higher quality than the first breakpoint list.
In particular, the list of target breakpoints may include one, two or more of the list attributes listed below; for example, the list attributes are phone number, license plate number, frame number, engine number, quoted price, insured, renewed or new car insurance risk.
The telephone number, the license plate number, the frame number and the engine number are the contact information and the vehicle related information which are input by the user on the vehicle insurance sale website. The above-mentioned attribute of the list of "quoted prices" means that when the user operates on the vehicle sales website, the vehicle sales website has provided quoted price information of a certain vehicle insurance according to the user operation, for example, a quoted price page showing vehicle insurance a. The list attribute of "covered" refers to attribute information that the user has covered at least one time on the vehicle sales website, and since this type of user is one of "old customers", the list attribute of "covered" is regarded as the key attribute information. The list attribute of "renewal" refers to that the user performs renewal-related operations on the vehicle sales website or browses renewal-related pages. The list attribute of "new car insurance risk" refers to that the user browses new car insurance risk on the car insurance sales website, for example, the user has been insured or browsed car insurance B, and when the user subsequently browses car insurance C on the car insurance sales website, the breakpoint list generated this time has the list attribute of "new car insurance risk".
102. Calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list;
after the target breakpoint lists are determined, attribute scores need to be calculated for respective list attributes under each target breakpoint list in each target breakpoint list. For example, there are two target breakpoint lists, and the target breakpoint list a has three list attributes, which are a1, a2 and a 3; the target breakpoint list b has three list attributes, namely b1, b2 and b 3; therefore, it is necessary to calculate attribute scores corresponding to each of a1, a2, a3, b1, b2, and b3, respectively. The calculated attribute scores corresponding to a1, a2 and a3 belong to a target breakpoint list a, and the calculated attribute scores corresponding to b1, b2 and b3 belong to a target breakpoint list b.
In this embodiment, the calculated attribute score corresponding to the list attribute is used for subsequently calculating the list score of each target breakpoint list, and the quality of the target breakpoint list is determined by the level of the list score, and the higher the list score is, the higher the quality of the target breakpoint list is. Therefore, the more useful the list attributes are required for the attribute scores corresponding to the respective list attributes, the higher the attribute score obtained by corresponding calculation should be.
Therefore, in this embodiment, the attribute score of the list attribute that needs to be calculated is positively correlated to the proportion of the list attribute in the successful sale history breakpoint list, that is, the larger the proportion of the list attribute in the successful sale history breakpoint list is, the higher the corresponding attribute score is; otherwise, the smaller the proportion of the list attribute in the successful sale history breakpoint list is, the lower the corresponding attribute score is. It can be understood that if 10 history breakpoint lists are successfully sold, wherein 9 breakpoint lists all have a list attribute d, that is, 90% of history breakpoint lists successfully sold have the list attribute d, it can be considered that the list attribute d greatly helps the successful sale of the history breakpoint lists; if the total number of the history breakpoint lists which are successfully sold is 10, only 1 breakpoint list has the list attribute e, that is, 10% of the history breakpoint lists which are successfully sold have the list attribute e, at this time, it can be considered that the sales success help of the list attribute e to the history breakpoint list is small. Needless to say, the ratio of the list attribute d to the list attribute e in the history breakpoint list of successful sale is larger than that of the list attribute e, and accordingly, the attribute score corresponding to the list attribute d should be higher than that of the list attribute e, for example, the attribute score may be: the attribute score corresponding to the list attribute d is 0.9, and the attribute score corresponding to the list attribute e is 0.1.
Specifically, the attribute score corresponding to the list attribute may be calculated by the following two methods, as shown in fig. 2 and fig. 3, which have advantages and disadvantages, and may be specifically selected and used according to actual situations.
The first method comprises the following steps: as shown in fig. 2, the step 102 may include:
201. determining the attribute of a target list of which the attribute score needs to be calculated currently;
202. counting a first quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
203. calculating a first proportion of the breakpoint list of the first number in the historical breakpoint list;
204. and determining the attribute score corresponding to the attribute of the target list according to the first ratio, wherein the larger the first ratio is, the higher the determined attribute score is.
In step 201, since each list attribute in each target breakpoint list needs to be individually calculated to obtain its corresponding attribute score, during calculation, multiple threads may be used, and each thread processes calculation of one list attribute. In one thread, the list attribute of which the attribute score needs to be calculated currently is determined as the target list attribute.
With respect to step 202, it can be understood that, for the collected breakpoint list, after the breakpoint list is assigned to the sales agent, the sales agent feeds back the processed results of the breakpoint list to the system. Therefore, the system can know which breakpoint lists in the historical breakpoint lists are successfully sold and which breakpoint lists are failed to be sold. In this embodiment, it is assumed that there are 100 historical breakpoint lists that are successfully sold on the system, and step 202 is to count how many breakpoint lists in the 100 historical breakpoint lists have the target list attribute. It can be known from the above contents that how much the first quantity can reflect how much the target list attribute helps the breakpoint list to successfully sell, and the larger the first quantity is, the larger the degree of help the target list attribute helps the breakpoint list to successfully sell is indicated.
For step 203, assuming a total of 100 historical breakpoints that were successfully sold on the system, the first number is 40, and the first ratio is 40%.
For step 204, the conversion relationship between the first ratio and the attribute score may be preset on the system. In particular, there may be a 1:1 relationship between the value of the first ratio and the value of the attribute score. That is, when the first ratio is 40%, the attribute score may be 0.4. In this embodiment, the conversion relationship between the first ratio and the attribute score is not specifically limited, but a positive correlation between the first ratio and the attribute score is required, that is, the larger the first ratio is, the higher the determined attribute score is; conversely, the smaller the first ratio, the lower the determined attribute score.
The second method comprises the following steps: as shown in fig. 3, the step 102 may include:
301. determining the attribute of a target list of which the attribute score needs to be calculated currently;
302. inquiring a preset attribute score corresponding to the target list attribute;
303. and determining the preset attribute score as an attribute score corresponding to the target list attribute.
Step 301 is similar to step 201 and will not be described herein again.
For step 302, the system may pre-store the preset attribute scores corresponding to the attributes of the target list, and the preset attribute scores corresponding to the attributes of the target list may be directly queried on the system when necessary.
As shown in fig. 4, the preset attribute score corresponding to the attribute of the target list may be preset through the following steps:
401. regularly counting a second quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
402. calculating a second proportion of the breakpoint list of the second quantity in the historical breakpoint list;
403. and determining a preset attribute score corresponding to the target list attribute according to the second proportion, wherein the larger the second proportion is, the higher the determined preset attribute score is.
For steps 401-403, the system may periodically make a second number of statistics, such as monthly, weekly, or daily statistics. The process of counting the second number is similar to the process of counting the first number in step 202, and is not repeated here. And after counting the second quantity of the attributes of the target list, correspondingly calculating a second proportion of the breakpoint list of the second quantity in the historical breakpoint list, and then determining a preset attribute score corresponding to the attributes of the target list according to the second proportion. In this embodiment, the conversion relationship between the second ratio and the preset attribute score is not specifically limited, but a positive correlation between the second ratio and the preset attribute score is required, that is, the larger the second ratio is, the higher the preset attribute score is determined to be; conversely, the smaller the second proportion, the lower the determined preset attribute score. Therefore, the system can calculate and store corresponding preset attribute scores for different target list attributes periodically, and when needed, the query system can obtain the preset attribute scores corresponding to the target list attributes.
For step 303, determining the queried preset attribute score as an attribute score corresponding to the attribute of the target list.
As can be seen from the above, the first method has a good or bad comparison with the second method. The first method has the advantages of strong real-time performance, and because the attribute score corresponding to each target list attribute is calculated in real time according to the historical breakpoint list data of successful sale on the system, the calculated attribute score corresponding to the target list attribute is more accurate and has stronger real-time performance, but the method has the defect of higher consumption of system operation resources; the second method has the advantages that the consumption of system operation resources is low, and the system only needs to calculate the preset attribute scores corresponding to all the list attributes in advance according to the successful sale history breakpoint list data and store the preset attribute scores. When a preset attribute score corresponding to a certain target list attribute needs to be obtained, the preset attribute score can be queried on a system without real-time calculation, so that the consumption of computing resources is low, but the defect is that the attribute score corresponding to the determined target list attribute is poor in real-time performance and possibly has the problem of low accuracy.
Therefore, when step 102 is executed, the first method or the second method can be selected and used according to actual situations.
For convenience of understanding, when the target breakpoint list includes list attributes such as a phone number, a license plate number, a frame number, an engine number, a quoted price, an insured price, a renewal insurance or a new car insurance risk, in an application scenario, attribute scores corresponding to the list attributes may be determined as follows:
1) if the statistics shows that all the successful sale breakpoint lists have complete telephone numbers, the attribute score corresponding to the list attribute of the telephone number can be 1;
2) if the breakpoint list of 80% successful sales is counted to have complete license plate numbers, the attribute score corresponding to the list attribute of the license plate number can be 0.8;
3) if the breakpoint list of 40% successful sales has three complete cars (license plate number, frame number, engine number), the attribute score corresponding to the list attribute of "three cars" may be 0.4;
4) if the 70% successful sale breakpoint list is counted to be quoted, the attribute score corresponding to the attribute of the list of quoted prices can be 0.7;
5) if the breakpoint list of which 80% of sales succeeds is counted as guaranteed, the attribute score corresponding to the attribute of the list of 'guaranteed' can be 0.8;
6) if the breakpoint list of which the sale is successful in 60% is counted to be the renewal, the attribute score corresponding to the attribute of the list of "renewal" may be 0.6;
7) if the breakpoint list of 40% successful sales is counted as the new car risk category, the attribute score corresponding to the list attribute of "new car risk category" may be 0.4.
103. And respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
after calculating the attribute score corresponding to each list attribute under each target breakpoint list in each target breakpoint list, the list score of each target breakpoint list needs to be calculated respectively. The list score of a target breakpoint list is determined by the attribute score corresponding to each list attribute under the target breakpoint list. Therefore, the step of determining the score of the list needs to be performed separately for each target breakpoint list: and determining the list score of one target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list.
Specifically, the above-mentioned ways of determining the score of the list in step 103 at least include the following four ways, and any one of the ways may be selected according to actual situations to determine the score of the list.
The first method is as follows: and calculating the sum of all attribute scores obtained by calculation under a target breakpoint list as the list score of the target breakpoint list. For example, if the attribute scores of a target breakpoint list are S1, S2, S3, … …, and Sn, respectively, the score S of the target breakpoint list isGeneral assembly＝S1+S2+S3+……+Sn。
The second method comprises the following steps: and performing linear accumulation on each attribute score obtained by calculation under a target breakpoint list to obtain a first accumulated value, and then calculating the mean value of the first accumulated value to each list attribute under the target breakpoint list to be used as the list score of the target breakpoint list. For example, each target breakpoint under a list of target breakpointsThe attribute scores are S1, S2, S3, … … and Sn respectively, and the first accumulated value of the target breakpoint list is SAddingS1+ S2+ S3+ … … + Sn. The score S of the target breakpoint listAre all made of＝SAdding/n＝(S1+S2+S3+……+Sn)/n。
The third method comprises the following steps: and calculating the variance of one target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the variance as the list score of the target breakpoint list. For example, if the attribute scores of a target breakpoint list are S1, S2, S3, … …, and Sn, respectively, the score S of the target breakpoint list isVariance (variance) 2＝((S1-SAre all made of)2+(S2-SAre all made of)2+…+(Sn-SAre all made of)2) N, wherein SAre all made of＝(S1+S2+S3+……+Sn)/n。
The method is as follows: and calculating the standard deviation of the target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the standard deviation as the list score of the target breakpoint list. For example, if the attribute scores of a target breakpoint list are S1, S2, S3, … …, and Sn, respectively, the score S of the target breakpoint list isStandard deviation ofAs defined above for SVariance (variance) 2The development of (1).
104. And sequentially distributing each target breakpoint list to the sales agents from high to low according to the list score corresponding to each target breakpoint list.
It can be understood that after the list scores corresponding to the target breakpoint lists are obtained through calculation, it can be known that the higher the list score is, the better the target breakpoint list is, the lower the list score is, the worse the target breakpoint list is, therefore, in order to improve the overall service conversion rate of the sales agent, the target breakpoint lists should be sequentially allocated to the sales agent according to the list scores from high to low, so that the breakpoint lists currently received by the sales agent can all be the currently best breakpoint list in the same batch, and further, the service conversion rate of the sales agent can be effectively improved.
In the embodiment, first, each target breakpoint list to be allocated to the sales agent is obtained, and each target breakpoint list contains more than one list attribute; then, calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list; then, executing the following steps for each target breakpoint list respectively: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list; and finally, sequentially distributing the target breakpoint lists to the sales agents according to the respective corresponding list scores of the target breakpoint lists from high to low. Therefore, the list attributes of the breakpoint lists can be respectively scored, and the list score of each breakpoint list is determined according to the score of each scored list attribute, so that the quality of the breakpoint lists is distinguished according to the list scores, the higher the list score is, the higher the quality of the breakpoint list is, when the breakpoint list is distributed to a sales seat, the breakpoint list is distributed from high to low according to the list score, namely, the breakpoint list with high quality is preferentially distributed. Therefore, the quality of the breakpoint list can be distinguished without manually distinguishing by the sales agent, so that the breakpoint list currently received by the sales agent is the breakpoint list with the currently best quality in the same batch, and the service conversion rate of the sales agent can be further improved.
It should be understood that, the sequence numbers of the steps in the foregoing embodiments do not imply an execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present invention.
The above mainly describes a method for processing a breakpoint list, and a detailed description will be given below of a processing apparatus for a breakpoint list.
Fig. 5 is a block diagram illustrating an embodiment of a breakpoint list processing apparatus according to an embodiment of the present invention.
In this embodiment, a processing apparatus for a breakpoint list includes:
a target list obtaining module 501, configured to obtain each target breakpoint list to be allocated to a sales agent, where each target breakpoint list includes more than one list attribute;
an attribute score calculating module 502, configured to calculate an attribute score corresponding to each list attribute of each target breakpoint list, where the level of the attribute score of the list attribute is positively correlated to the size of the proportion of the list attribute in the successful sale history breakpoint list;
a list score calculating module 503, configured to perform, on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
and a breakpoint list distribution module 504, configured to sequentially distribute each target breakpoint list to the sales agents according to the respective list scores corresponding to each target breakpoint list from high to low.
Further, the attribute score calculation module may include:
the first attribute determining unit is used for determining the attribute of a target list of which the attribute score needs to be calculated currently;
the first quantity counting unit is used for counting the first quantity of the breakpoint list with the target list attribute in the successful selling history breakpoint list;
the first proportion calculation unit is used for calculating a first proportion of the breakpoint list of the first number in the historical breakpoint list;
and the first score determining unit is used for determining the attribute score corresponding to the attribute of the target list according to the first ratio, wherein the greater the first ratio is, the higher the determined attribute score is.
Further, the attribute score calculation module may include:
the second attribute determining unit is used for determining the attribute of a target list of which the attribute score needs to be calculated currently;
the score query unit is used for querying a preset attribute score corresponding to the target list attribute;
the second score determining unit is used for determining the preset attribute score as an attribute score corresponding to the attribute of the target list;
the preset attribute score corresponding to the target list attribute may be preset by:
the second quantity counting module is used for regularly counting the second quantity of the breakpoint list with the target list attribute in the successful marketing history breakpoint list;
the second proportion calculation module is used for calculating a second proportion of the breakpoint list of the second quantity in the historical breakpoint list;
and the preset score determining module is used for determining the preset attribute score corresponding to the target list attribute according to the second proportion, wherein the larger the second proportion is, the higher the determined preset attribute score is.
Further, the list score calculation module may include:
the first list score calculating unit is used for calculating the sum of all attribute scores obtained by calculation under a target breakpoint list as the list score of the target breakpoint list;
or
The accumulation unit is used for carrying out linear accumulation on each attribute score obtained by calculation under a target breakpoint list to obtain a first accumulated value;
a second list score calculating unit, configured to calculate a mean value of the first accumulated value to each list attribute under the target breakpoint list as a list score of the target breakpoint list;
or
A third list score calculating unit, configured to calculate a variance of one target breakpoint list according to each attribute score calculated under the one target breakpoint list, where the variance is used as a list score of the one target breakpoint list;
or
And the fourth list score calculating unit is used for calculating the standard deviation of one target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and the standard deviation is used as the list score of the target breakpoint list.
Further, the list of target breakpoints may include one, two, or more of the list attributes listed below;
the list attributes are telephone numbers, license plate numbers, frame numbers, engine numbers, quoted prices, insured times, continuous guarantees or new car insurance risk types.
Fig. 6 is a schematic diagram of a server according to an embodiment of the present invention. As shown in fig. 6, the server 6 of this embodiment includes: a processor 60, a memory 61 and a computer program 62 stored in said memory 61 and executable on said processor 60, such as a program performing a method of processing a breakpoint list. The processor 60 executes the computer program 62 to implement the steps in the above-mentioned embodiments of the processing method for the breakpoint list, such as the steps 101 to 104 shown in fig. 1. Alternatively, the processor 60, when executing the computer program 62, implements the functions of the modules/units in the above-mentioned device embodiments, such as the functions of the modules 501 to 504 shown in fig. 5.
Illustratively, the computer program 62 may be partitioned into one or more modules/units that are stored in the memory 61 and executed by the processor 60 to implement the present invention. The one or more modules/units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution of the computer program 62 in the server 6.
The server 6 may be a local server, a cloud server, or other computing device. The server may include, but is not limited to, a processor 60, a memory 61. Those skilled in the art will appreciate that fig. 6 is merely an example of a server 6 and does not constitute a limitation of the server 6, and may include more or fewer components than shown, or some components in combination, or different components, e.g., the server may also include input output devices, network access devices, buses, etc.
The Processor 60 may be a Central Processing Unit (CPU), other general purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, discrete hardware component, etc. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The memory 61 may be an internal storage unit of the server 6, such as a hard disk or a memory of the server 6. The memory 61 may also be an external storage device of the server 6, such as a plug-in hard disk, a Smart Media Card (SMC), a Secure Digital (SD) Card, a Flash memory Card (Flash Card) and the like provided on the server 6. Further, the memory 61 may also include both an internal storage unit of the server 6 and an external storage device. The memory 61 is used for storing the computer program and other programs and data required by the server. The memory 61 may also be used to temporarily store data that has been output or is to be output.
It is clear to those skilled in the art that, for convenience and brevity of description, the specific working processes of the above-described systems, apparatuses and units may refer to the corresponding processes in the foregoing method embodiments, and are not described herein again.
In the above embodiments, the descriptions of the respective embodiments have respective emphasis, and reference may be made to the related descriptions of other embodiments for parts that are not described or illustrated in a certain embodiment.
Those of ordinary skill in the art would appreciate that the modules, elements, and/or method steps of the various embodiments described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the above-described apparatus embodiments are merely illustrative, and for example, the division of the units is only one logical division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, devices or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and other various media capable of storing program codes.
The above-mentioned embodiments are only used for illustrating the technical solutions of the present invention, and not for limiting the same; although the present invention has been described in detail with reference to the foregoing embodiments, it will be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some technical features may be equivalently replaced; and such modifications or substitutions do not depart from the spirit and scope of the corresponding technical solutions of the embodiments of the present invention.
Claims (8)
1. A breakpoint list processing method is characterized by comprising the following steps:
obtaining target breakpoint lists to be distributed to sales agents through a server, wherein each target breakpoint list comprises more than one list attribute, the breakpoint lists are operations of browsing on a sales website by a user, and the target breakpoint lists comprise telephone numbers, license plate numbers, frame numbers, engine numbers, quoted prices, insured prices, continuous insurance or new car insurance risk types;
calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list;
and respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
sequentially distributing each target breakpoint list to a sales agent according to the respective corresponding list score of each target breakpoint list from high to low;
the calculating the attribute score corresponding to each list attribute of each target breakpoint list comprises:
determining the attribute of a target list of which the attribute score needs to be calculated currently;
inquiring a preset attribute score corresponding to the target list attribute;
determining the preset attribute score as an attribute score corresponding to the target list attribute;
the preset attribute score corresponding to the target list attribute is preset through the following steps:
regularly counting a second quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
calculating a second proportion of the breakpoint list of the second quantity in the historical breakpoint list;
and determining a preset attribute score corresponding to the target list attribute according to the second proportion, wherein the larger the second proportion is, the higher the determined preset attribute score is, the preset attribute score is calculated and stored by the system regularly aiming at different target list attributes, and the preset attribute score can be obtained by querying the system.
2. The method for processing breakpoint lists according to claim 1, wherein the calculating an attribute score corresponding to each list attribute of each target breakpoint list includes:
determining the attribute of a target list of which the attribute score needs to be calculated currently;
counting a first quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
calculating a first proportion of the breakpoint list of the first number in the historical breakpoint list;
and determining the attribute score corresponding to the attribute of the target list according to the first ratio, wherein the larger the first ratio is, the higher the determined attribute score is.
3. The method for processing breakpoint lists according to claim 1, wherein the determining the list score of one target breakpoint list according to each attribute score calculated under the one target breakpoint list includes:
calculating the sum of all attribute scores obtained by calculation under a target breakpoint list as the list score of the target breakpoint list;
or
Performing linear accumulation on each attribute score obtained by calculation under a target breakpoint list to obtain a first accumulated value;
calculating the mean value of the first accumulated value to each list attribute under the target breakpoint list as the list score of the target breakpoint list;
or
Calculating the variance of one target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the variance as the list score of the target breakpoint list;
or
And calculating the standard deviation of the target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the standard deviation as the list score of the target breakpoint list.
4. The method for processing the breakpoint list according to any one of claims 1 to 3, wherein the target breakpoint list includes one, two or more of the list attributes listed below;
the list attributes are telephone numbers, license plate numbers, frame numbers, engine numbers, quoted prices, insured times, continuous guarantees or new car insurance risk types.
5. A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method for processing a breakpoint list according to any one of claims 1 to 4.
6. A server comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor when executing the computer program implements the steps of:
obtaining target breakpoint lists to be distributed to sales agents through a server, wherein each target breakpoint list comprises more than one list attribute, the breakpoint lists are operations of browsing on a sales website by a user, and the target breakpoint lists comprise telephone numbers, license plate numbers, frame numbers, engine numbers, quoted prices, insured prices, continuous insurance or new car insurance risk types;
calculating attribute scores corresponding to the list attributes of each target breakpoint list, wherein the attribute scores of the list attributes are positively correlated with the proportion of the list attributes in the successful sale history breakpoint list;
and respectively executing the following steps on each target breakpoint list: determining the list score of a target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list;
sequentially distributing each target breakpoint list to a sales agent according to the respective corresponding list score of each target breakpoint list from high to low;
the calculating the attribute score corresponding to each list attribute of each target breakpoint list comprises:
determining the attribute of a target list of which the attribute score needs to be calculated currently;
inquiring a preset attribute score corresponding to the target list attribute;
determining the preset attribute score as an attribute score corresponding to the target list attribute;
the preset attribute score corresponding to the target list attribute is preset through the following steps:
regularly counting a second quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
calculating a second proportion of the breakpoint list of the second quantity in the historical breakpoint list;
and determining a preset attribute score corresponding to the target list attribute according to the second proportion, wherein the larger the second proportion is, the higher the determined preset attribute score is.
7. The server according to claim 6, wherein the calculating an attribute score corresponding to each list attribute of each target breakpoint list comprises:
determining the attribute of a target list of which the attribute score needs to be calculated currently;
counting a first quantity of breakpoint lists with the target list attributes in a history breakpoint list which is successfully sold;
calculating a first proportion of the breakpoint list of the first number in the historical breakpoint list;
and determining the attribute score corresponding to the attribute of the target list according to the first ratio, wherein the larger the first ratio is, the higher the determined attribute score is.
8. The server according to claim 6 or 7, wherein the determining the list score of one target breakpoint list according to each attribute score calculated under the one target breakpoint list comprises:
calculating the sum of all attribute scores obtained by calculation under a target breakpoint list as the list score of the target breakpoint list;
or
Performing linear accumulation on each attribute score obtained by calculation under a target breakpoint list to obtain a first accumulated value;
calculating the mean value of the first accumulated value to each list attribute under the target breakpoint list as the list score of the target breakpoint list;
or
Calculating the variance of one target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the variance as the list score of the target breakpoint list;
or
And calculating the standard deviation of the target breakpoint list according to each attribute score obtained by calculation under the target breakpoint list, and taking the standard deviation as the list score of the target breakpoint list.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710605626.9A CN107688959B (en) | 2017-07-24 | 2017-07-24 | Breakpoint list processing method, storage medium and server |
| PCT/CN2018/074883 WO2019019596A1 (en) | 2017-07-24 | 2018-01-31 | Breakpoint list processing method, device, server and medium |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201710605626.9A CN107688959B (en) | 2017-07-24 | 2017-07-24 | Breakpoint list processing method, storage medium and server |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN107688959A CN107688959A (en) | 2018-02-13 |
| CN107688959B true CN107688959B (en) | 2020-12-29 |
Family
ID=61152942
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201710605626.9A Active CN107688959B (en) | 2017-07-24 | 2017-07-24 | Breakpoint list processing method, storage medium and server |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN107688959B (en) |
| WO (1) | WO2019019596A1 (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109472452B (en) * | 2018-10-11 | 2023-09-05 | 平安科技（深圳）有限公司 | Intelligent dispatching method, intelligent dispatching device, computer equipment and storage medium |
| CN111078684B (en) * | 2019-11-08 | 2023-07-18 | 泰康保险集团股份有限公司 | Policy fault compensation method, device, medium and equipment |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104462333A (en) * | 2014-12-03 | 2015-03-25 | 上海耀肖电子商务有限公司 | Shopping search recommending and alarming method and system |
| CN105225027A (en) * | 2015-08-26 | 2016-01-06 | 上海银天下科技有限公司 | Customer allocation method and system |
| CN105247507A (en) * | 2013-05-31 | 2016-01-13 | 惠普发展公司，有限责任合伙企业 | Influence score of a brand |
| CN106095738A (en) * | 2015-04-27 | 2016-11-09 | 奥多比公司 | Recommendation tables single slice |
| CN106777132A (en) * | 2016-12-18 | 2017-05-31 | 深圳市辣妈帮科技有限公司 | Data processing method and device |
Family Cites Families (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP5846002B2 (en) * | 2012-03-28 | 2016-01-20 | 富士通株式会社 | Server apparatus, information providing program, information providing method, and information providing system |
| US10460247B2 (en) * | 2015-12-08 | 2019-10-29 | Adobe Inc. | Attribute weighting for media content-based recommendation |
| CN105976207A (en) * | 2016-05-11 | 2016-09-28 | 山东大学 | Information search result generation method and system based on multi-attribute dynamic weight distribution |
-
2017
- 2017-07-24 CN CN201710605626.9A patent/CN107688959B/en active Active
-
2018
- 2018-01-31 WO PCT/CN2018/074883 patent/WO2019019596A1/en active Application Filing
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105247507A (en) * | 2013-05-31 | 2016-01-13 | 惠普发展公司，有限责任合伙企业 | Influence score of a brand |
| CN104462333A (en) * | 2014-12-03 | 2015-03-25 | 上海耀肖电子商务有限公司 | Shopping search recommending and alarming method and system |
| CN106095738A (en) * | 2015-04-27 | 2016-11-09 | 奥多比公司 | Recommendation tables single slice |
| CN105225027A (en) * | 2015-08-26 | 2016-01-06 | 上海银天下科技有限公司 | Customer allocation method and system |
| CN106777132A (en) * | 2016-12-18 | 2017-05-31 | 深圳市辣妈帮科技有限公司 | Data processing method and device |
Also Published As
| Publication number | Publication date |
|---|---|
| WO2019019596A1 (en) | 2019-01-31 |
| CN107688959A (en) | 2018-02-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN107391538B (en) | Click data acquisition, processing and display method, device, equipment and storage medium | |
| CN108492188B (en) | Client recommendation method, device, equipment and storage medium | |
| CN112000675A (en) | Market data updating method and device, terminal equipment and storage medium | |
| CN112364004B (en) | Data warehouse-based policy data processing method, device and storage medium | |
| CN107688959B (en) | Breakpoint list processing method, storage medium and server | |
| CN111709777A (en) | Payment mode recommendation method, system, terminal device and storage medium | |
| WO2019024475A1 (en) | Breakpoint list distribution method and apparatus, and server and medium | |
| CN111597237B (en) | Method and device for generating data query result, electronic equipment and storage medium | |
| CN110489705B (en) | Method, device, terminal equipment and storage medium for calculating brokerage fee | |
| CN107330709B (en) | Method and device for determining target object | |
| CN112102036A (en) | Information recommendation method and device and storage medium | |
| CN107784062B (en) | Product data processing system and method | |
| CN109377391B (en) | Information tracking method, storage medium and server | |
| CN116503092A (en) | User reservation intention recognition method and device, electronic equipment and storage medium | |
| CN115689245A (en) | Method and device for determining rider's saddle bill quantity in different scenes | |
| CN115293913A (en) | Early warning method and device for payment refusal risk of commercial draft acceptance person | |
| CN114547179A (en) | Asset data display method and device, computer equipment and storage medium | |
| CN108259552B (en) | Case processing system and method | |
| CN110675268A (en) | Risk client identification method and device and server | |
| CN111353786A (en) | Product after-sale information acquisition method, user terminal and server | |
| CN112199191A (en) | Data matching method, device and system for virtual transaction | |
| CN109658127B (en) | Breakpoint information processing method and device | |
| CN108256719B (en) | Resource processing system and method | |
| CN109727144A (en) | Insure Incidence calculus method, apparatus, equipment and computer readable storage medium | |
| CN113344630B (en) | Resource allocation method, device, electronic equipment and storage medium |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |